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Sports Probabilities MCP

by io.github.commodus67
OverviewDiscussionsAdmin

What-if simulation

run_scenario

Re-run the Monte Carlo under your own assumptions and show which teams move the most against the current baseline. Change how hard a short record is regressed to .500, how much weight point or run differential carries, home advantage, or how uncertain the model is about true talent. This starts a fresh simulation rather than reading the cache.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sport": "<sport>",
      "parameters": "<parameters>"
    },
    "name": "run_scenario"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many teams to return. Default 40.
sportYesnfl, mlb, nhl, nba or soccer.
leagueNoSoccer only. A league name or ESPN slug, for example "Premier League" or "esp.1". Ignored for every other sport.
metricNoWhich probability to compare before and after.
parametersYesParameters to change. NFL: iterations, season, regressionGames, pythagoreanWeight, homeFieldAdvantage, strengthUncertainty, tieProbability. MLB: the same minus tieProbability. NHL: iterations, season, regressionGames, pythagoreanWeight, homeIceAdvantage, strengthUncertainty, overtimeProbability, overtimeDamping, priorCarryover. NBA: iterations, season, regressionGames, pythagoreanWeight, homeAdvantage, strengthUncertainty, priorCarryover. Soccer: iterations, historySeasons, homeAdvantage.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral disclosure burden. It usefully states that this starts a fresh simulation rather than reading the cache, but it does not discuss runtime, determinism, persistence, or output shape beyond team movement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main action and result, then lists the assumptions that can be changed. Every sentence contributes useful context, including the fresh-versus-cache distinction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a nested parameter object, no annotations, and no output schema, so the description needs to establish both scenario behavior and expected result. It does so at a high level by explaining custom assumptions and team movement versus baseline, though it leaves detailed return values and operational traits unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds conceptual meaning beyond the schema by explaining regression to .500, point or run differential weight, home advantage, and uncertainty about true talent. It does not clarify every parameter, such as limit or metric, but it meaningfully explains the main scenario controls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb and resource: it says to re-run the Monte Carlo under custom assumptions and show teams moving against the current baseline. It also distinguishes the operation from cached reads by stating that it starts a fresh simulation rather than reading the cache.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: use this for what-if scenarios where the caller supplies assumptions and wants to see movement versus baseline. It also contrasts fresh simulation with cache reading, though it does not explicitly name sibling alternatives or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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